Power equipment, for example, can be power transformer, machine and so on, whose losses vary with its loading level, and substantial part of its losses is converted into heat. Due to higher power ratings, power equipment is often cooled by forced-air cooling, forced-oil cooling, water-cooling, or combinations of these. Therefore, the power equipment needs a cooling system which, for example, can be an arrangement of at least one fan or blower pumping cooling-air or liquid coolants as oil and water. The practice of slowing down or speeding up cooling system to keep temperature of a power equipment within a predetermined range is common and there are numerous technologies that accomplish this.
An operation efficiency of the power equipment is influenced by several parameters, such as the costs related to power lose, life lose and noise-reduction. To optimize the power equipment operation efficiency as an objective for optimization-based design, these parameters have to be adjusted with conflicting constraints. For example, normally, lower winding temperature leads to the lower copper loss of winding. However, the power consumption of the cooling system will be higher at the same time, meaning that the overall efficiency, considering both power equipment winding and the cooling system itself, might be less optimal. Besides efficiency, the variation of the winding temperature is also one key factor which will affect the lifecycle of the power equipment. The more frequent the temperature varies, the faster the power equipment aging will be. It could be so that the efficiency of the power equipment is optimized, however at a cost of shortened lifetime. For a power equipment operated at urban area, noise level is also one important criterion to consider in order to reduce the impact on the neighbouring residents especially at night.
A variable speed cooling system controller has been developed that is intended for optimization of the operation efficiency of power transformer. One example is described in Pat. WO 2015/058354, wherein the concept of objective optimization considering the constraints of its power loss, life loss and noise for transformer cooling control has been disclosed. The solution involves preprocessing the initial data input by user; collecting the on-line data, and based on a quantitative model according to criteria specified for a time interval, calculating the optimized control command to meet the requirement of the transformer loss, top-oil temperature variation and noise; and executing the control actions by controlling a controllable switch and/or sending a control command to a variable frequency driver activating the cooling system. Such solution enables improving operation efficiency of the transformer by cooling control considering transformer copper loss, motor-fans power consumption and speed regulation of variable frequency drive. With this approach, a computer is used to search for the best cooling control realizing speed regulation for the motor-fan loads selectively with low capital investment on variable frequency drive in an efficient manner.
According to the quantitative model described in Pat. WO 2015/058354, the objective of its criteria to be optimized concerns with the power transformer operation efficiency in a certain time interval of a multiple continuous time intervals consisting of a load cycle. The load cycle can be a period of time, where the loading level of the power equipment appears in a substantially cyclic manner, for example 24 hours. The load cycle can be divided into several time units either with equal length or unequal, for example 24 time intervals each lasting for one hour. This quantitative model only predicts the power transformer operation efficiency respectively in terms of each of the time intervals. However, because the response of interest for previous time intervals will have effect upon the subsequent, separate optimization design based on the quantitative model might not suffice for optimal criteria for an operation efficiency objective concerning a load cycle of a power equipment. For example, the cooling optimization at time interval n might lead to higher initial temperature for time interval n+1. Such temperature difference will accumulate step-by-step for the computation of the response of interest concerning the time intervals of the load cycle subsequent to the initial time interval n, which will gradually increase transformer power loss and life loss. Therefore, even if the optimization algorithm in terms of each of the time intervals can achieve the best result at every time internal, not necessarily the best for an entire load cycle.